Abstract
The emerging discipline of Data Science poses several challenges for teams conducting projects in the field as notably, the majority of Data Science teams fail to deliver the expected outcomes. To improve the results, researchers tried to adapt agile project methodologies like Scrum for Data Science projects. Scrum in particular is often implemented due its success in software engineering. However, the basic Scrum framework has proven itself to be too strict for Data Science, due to frequent unpredictabilities of Data Science tasks. Consequently, adaptions were made to traditional Scrum to make it more suitable for the new challenges. This article discusses further adaptations and suggests that Scrum in itself is usable in Data Science, however, additional adaptations of the core concepts need to be envisioned.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
